AI and Machine Learning Foundations for Business Leaders

In this lesson, we contrast traditional rule-based logic (If-Then-Else) with ML’s data-driven approaches. This lesson sets the stage by explaining why math underlies all of machine learning. Whether it is supervised or unsupervised learning, the “model” is a mathematical function being optimised. This lesson explains the basics of how the different supervised and unsupervised machine learning algorithms ‘fit a function to known data’, so that after it is ‘trained’ it can be used on new data. This is in stark contrast to traditional programming where we rely on hard-coded logic.

We introduce the concept of a model as a function that takes inputs (features) and produces predictions (regression, classification, etc.). We clarify what model parameters are, and how fitting a model means adjusting these parameters to better approximate the mapping between inputs and outputs. Additionally, we ensure a proper understanding of important concepts such as labelled data, algorithms, loss functions, overfitting and model performance evaluation.

We introduce the concept of a model not as a function that predicts outputs for inputs, but instead organizes data based on relationships between the data items. We clarify what model parameters are, and how ‘fitting a model’ means adjusting these parameters to better approximate the underlying data relationships. Additionally, we ensure proper understanding of important concepts such as unlabelled data, algorithms, loss functions, overfitting and model performance evaluation.

This lesson introduces fascinating real-world examples of where reinforcement learning (RL) is powering the most mind-blowing advancements of our time. This includes, but is not limited to robotics, game-playing agents, landing rockets, controlling nuclear fusion reactions, high-frequency algorithmic trading and advanced recommender systems (like Netflix or Instagram).

This lesson introduces the powerful role of reinforcement learning and AI-driven personalization in transforming recommendation systems across entertainment, e-commerce, and loyalty programs. By examining real-world examples from Netflix, Spotify, and leading retailers, it highlights how personalized recommendations significantly enhance customer engagement and business profitability.

This lesson outlines the fundamentals of traditional recommender systems, including collaborative filtering, matrix factorization, and content-based techniques. It explores how these methods evolved to address user preferences in loyalty and rewards programmes, paving the way for more advanced approaches.

Here, we introduce the core principles of reinforcement learning or RL, highlighting its focus on long-term outcomes and sequential decision-making. We show why RL is uniquely suited for loyalty programmes by discussing how it can continuously learn and adapt based on user interactions and feedback.

This lesson focuses on practical adoption, showcasing how leading companies have attempted to leverage RL in coalition reward programmes, credit card offers, and retail loyalty ecosystems. We discuss the tangible benefits, common pitfalls, and emerging trends in applying RL-based personalization strategies to drive sustained customer engagement.

This video introduces the speakers/specialists, Irlon and Eric. The high-level agenda/script is attached as a PDF. Irlon's responses to the questions outlined in the agenda are summarised in the attached PowerPoint presentation.